Next-generation sequencing (NGS) refers to a group of high-throughput technologies used to analyse DNA and RNA. Over the past two decades, NGS has become widely adopted across life science research, enabling researchers to generate genetic and genomic data at a scale that was previously difficult to achieve.
Today, NGS supports a broad range of applications, including cancer research, infectious disease surveillance, microbiome studies, rare disease research and gene expression analysis. Advances in both short-read and long-read sequencing technologies have expanded the types of biological questions that can be investigated, from identifying single nucleotide variants to resolving complex structural changes within genomes. As a result, NGS has become an important tool for understanding biological systems and supporting scientific discovery.
What is NGS?
Next-generation sequencing (NGS) is a method used to determine the sequence of DNA or RNA. Unlike traditional Sanger sequencing, which analyses individual DNA fragments, NGS enables millions of fragments to be sequenced in parallel. This approach allows researchers to generate large volumes of sequencing data efficiently and cost-effectively.
The term NGS encompasses a range of sequencing technologies and platforms. While short-read sequencing remains widely used across genomics workflows, long-read sequencing technologies have become increasingly important for applications that require improved resolution of complex genomic regions, structural variants and full-length transcripts.
How does NGS work?
Although sequencing platforms differ in their underlying chemistry, most NGS workflows follow four key stages:
1. Sample preparation
The process begins with extraction of DNA or RNA from a biological sample. Depending on the application, RNA may be converted into complementary DNA (cDNA) before sequencing.
2. Library preparation
The nucleic acid is fragmented into smaller pieces and specialised adapters are attached. These adapters enable the fragments to bind to the sequencing instrument and allow individual samples to be identified during analysis.
3. Sequencing
The prepared library is loaded onto a sequencing platform. Millions of fragments are sequenced in parallel, generating large numbers of reads. Modern sequencing systems can produce data ranging from targeted gene panels to complete genome sequences in a single run.
4. Bioinformatic analysis
Once sequencing is complete, computational tools align the reads to a reference genome or assemble them into longer sequences. Researchers can then identify genetic variants, measure gene expression levels or characterise entire microbial communities.
Short-read versus long-read sequencing
One of the most significant developments in recent years has been the rise of long-read sequencing technologies.
Traditional NGS platforms typically generate short reads measuring a few hundred base pairs in length. These technologies offer exceptional accuracy, high throughput and cost efficiency, making them ideal for many routine genomic applications.
Long-read sequencing technologies, by contrast, can produce reads spanning thousands to hundreds of thousands of base pairs. These longer reads simplify genome assembly and make it easier to study repetitive genomic regions, structural variants and complex transcript isoforms.
| Feature | Short-read sequencing | Long-read sequencing |
|---|---|---|
| Typical read length | Hundreds of base pairs | Thousands to hundreds of thousands of base pairs |
| Key strengths | High accuracy, scalability and throughput | Resolution of structural variants and complex genomic regions |
| Common applications | RNA-seq, whole-genome sequencing, exome sequencing, targeted panels | De novo genome assembly, transcript isoform analysis, structural variant detection |
| Typical use case | Large-scale routine sequencing projects | Investigating complex genomes and previously unresolved regions |
Rather than competing technologies, short- and long-read sequencing are increasingly used together to provide a more complete picture of biological systems.
What are the main applications of NGS?
As sequencing technologies have matured, the range of NGS applications has expanded dramatically.
- Cancer research and precision oncology: identifying genetic alterations, discovering biomarkers and supporting personalised treatment strategies.
- Infectious disease surveillance: detecting pathogens, monitoring emerging variants and investigating outbreaks.
- Transcriptomics and gene expression analysis: measuring gene activity and studying transcript diversity through RNA sequencing (RNA-seq).
- Rare disease and human genomics research: uncovering disease-causing mutations, structural variants and previously unresolved regions of the genome.
- Microbiome and metagenomics studies: analysing complex microbial communities from environmental, agricultural and clinical samples.
Single-cell and multiomics research: exploring cellular heterogeneity and integrating genomic, transcriptomic and epigenetic data to better understand biological systems.
Why is NGS still evolving?
Although NGS is now firmly established across life science research, innovation within the field continues at a rapid pace.
Advances in sequencing chemistry, instrument performance, automation and computational analysis are increasing data quality while reducing turnaround times. At the same time, long-read sequencing is becoming more accurate, scalable and accessible, enabling researchers to explore regions of the genome that were once difficult or impossible to study.
As sequencing technologies become more accessible, researchers can apply genomic approaches across a broader range of sample types and applications. The continued development of sequencing platforms, library preparation methods and bioinformatic tools is helping to expand the scope of genomics research.
Supporting NGS workflows from sample to discovery
Generating reliable sequencing data depends on multiple stages of the workflow, from sample preparation and library construction to quality control and downstream analysis. The quality of materials and methods used at each step can influence experimental outcomes and data reproducibility.
Researchers therefore require workflow solutions that are appropriate for their application, whether performing whole-genome sequencing, transcriptomic analysis, microbiome studies or emerging multiomics workflows. Selecting suitable reagents, consumables and preparation methods can help support consistent results and efficient sequencing workflows. Explore Cambridge Bioscience's NGS workflow solutions.
References and further reading
Illumina. Next Generation Sequencing for Beginners.
Kamps, R., Brandão, R.D., van den Bosch, B.J., Paulussen, A.D., Xanthoulea, S., Blok, M.J. and Romano, A., 2017. Next-generation sequencing in oncology: genetic diagnosis, risk prediction and cancer classification. International journal of molecular sciences, 18(2), p.308.
Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet. 2020 Oct;21(10):597-614. doi: 10.1038/s41576-020-0236-x. Epub 2020 Jun 5. PMID: 32504078; PMCID: PMC7877196.
Rausch T, Marschall T, Korbel JO. The impact of long-read sequencing on human population-scale genomics. Genome Res. 2025 Apr 14;35(4):593-598. doi: 10.1101/gr.280120.124. PMID: 40228902; PMCID: PMC12047236.